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A. Criminisi, Blake, A., Rother, C., Shotton, J., and Torr, P. H. S., “Efficient dense stereo with occlusions for new view-synthesis by four-state dynamic programming”, International Journal of Computer Vision, vol. 71, pp. 89–110, 2007.

A. Criminisi, Shotton, J., Blake, A., and Torr, P., “Efficient dense stereo and novel-view synthesis for gaze manipulation in one-to-one teleconferencing”, 2004.

D. Cremers and Schnörr, C., “Statistical Shape Knowledge in Variational Motion Segmentation”, Image and Vision Comp., vol. 21, pp. 77-86, 2003.

D. Cremers and Schnörr, C., “Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 472–480.

D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, pp. 1929–1943, 2003.

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics via Kernel Spaces”, in Mustererkennung 2001, Munich, Germany, 2001, vol. 2191, pp. 269–276.

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics in Mumford-Shah Based Segmentation”, in Computer Vision – ECCV 2002), 2002, vol. 2351, pp. 93–108.

D. Cremers, Schnörr, C., and Weickert, J., “Diffusion–Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework”, in IEEE First Workshop on Variational and Level Set Methods in Computer Vision, Vancouver, Canada, 2001, pp. 237–244.

D. Cremers, Schnörr, C., Weickert, J., and Schellewald, C., “Learning Translation Invariant Shape Knowledge for Steering Diffusion-Snakes”, in 3rd Workshop on Dynamic Perception, Berlin, Germany, 2000, vol. 9, pp. 117–122.

D. Cremers, Schnörr, C., Weickert, J., and Schellewald, C., “Diffusion Snakes Using Statistical Shape Knowledge”, in Proc. Algebraic Frames for the Perception-Action Cycle, Kiel, 2000, vol. 1888, pp. 164–174.

D. Cremers, Sochen, N., and Schnörr, C., “Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling”, in Scale Space Methods in Computer Vision, 2003, vol. 2695, p. 388--400.

Technical Report (451.82 KB)

D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, p. 1929--1943, 2003.

Technical Report (1.67 MB)

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics via Kernel Spaces”, in Mustererkennung 2001, 2001, vol. 2191, p. 269--276.

Technical Report (324.55 KB)

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics in Mumford-Shah Based Segmentation”, in Computer Vision -- ECCV 2002), 2002, vol. 2351, p. 93--108.

Technical Report (636.58 KB)

D. Cremers, Sochen, N., and Schnörr, C., “Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling”, in Scale Space Methods in Computer Vision, 2003, vol. 2695, pp. 388–400.

D. Cremers, Sochen, N., and Schnörr, C., “Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation”, in Computer Vision – ECCV 2004, 2004, vol. 3024, pp. 74-86.

D. Cremers, Sochen, N., and Schnörr, C., “Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation”, ijcv, vol. 66, pp. 67-81, 2006.

D. Cremers, Tischhäuser, F., Weickert, J., and Schnörr, C., “Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford–Shah functional”, Int. J. Computer Vision, vol. 50, pp. 295–313, 2002.

R. Chellappa and Machinery., Afor Comput, “Proceedings - 7th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2010”, ACM International Conference Proceeding Series. ACM, 2010.

L. Cerrone, Zeilmann, A., and Hamprecht, F. A., “End-to-End Learned Random Walker for Seeded Image Segmentation”, CVPR. Proceedings. pp. 12559-12568, 2019.

L. Cerrone, “Deep End-to-End Learning of a Diffusion Process for Seeded Image Segmentation”, Heidelberg University, 2018.

Y. Censor, Petra, S., and Schnörr, C., “Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case”, J. Appl. Numer. Optimization (in press; arXiv:1911.05498), vol. 2, pp. 15-62, 2020.

Y. Censor, Gibali, A., Lenzen, F., and Schnörr, C., “The Implicit Convex Feasibility Problem and Its Application to Adaptive Image Denoising”, J. Comp. Math., vol. 34, pp. 608-623, 2016.

Y. Censor, Petra, S., and Schnörr, C., “Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case”, preprint: arXiv, 2019.

A. Cavallo, “Four dimensional particle tracking in biological dynamic processes”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2002.

H. Carstens, “Ein Skalenraumverfahren zur Orts/Wellenzahl-Raum-Analyse winderzeugter Wasserwellen”, IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1998.

M. F. Carlsohn, Menze, B. H., Kelm, B. Michael, Hamprecht, F. A., Kercek, A., Leitner, R., and Polder, G., “Color image processing”, vol. 7(17), R. Lukac and Plataniotis, K. N., Eds. CRC Press, 2006, pp. 393-419.

B. Jähne and Jähne, B., “Evaluation of a two-scale model using extensive radar backscatter and wave measurements in a large wind-wave flume”, in Proceedings IGARSS '91, 1991, vol. 2, p. 885--888.

C. Cali, Baghabra, J., Boges, D. J., Holst, G. R., Kreshuk, A., Hamprecht, F. A., Srinivasan, M., Lehväslaiho, H., and Magistretti, P. J., “Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues”, Journal of Comparative Neurology, vol. 524, pp. 23-38, 2015.